Modified maximum likelihood predictors of future order statistics from normal samples

DOI10.1016/S0167-9473(96)00082-5zbMath0900.62255MaRDI QIDQ1391335

Mohammad Zayed Raqab

Publication date: 22 July 1998

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

zbMATH Keywords

Order statisticsPredictionNormal distributionMaximum likelihood predictorModified maximum likelihood predictor

Mathematics Subject Classification ID

Order statistics; empirical distribution functions (62G30) Probabilistic methods, stochastic differential equations (65C99)

Related Items (11)

Unnamed Item ⋮ Prediction of censored exponential lifetimes in a simple step-stress model under progressive type II censoring ⋮ Comparison of estimators and predictors based on modified weibull records: Bayesian and non-Bayesian approaches ⋮ Bayesian inference and prediction of order statistics for a type-II censored Weibull distribution ⋮ Bayesian Prediction for Progressively Type-II Censored Data from the Rayleigh Model ⋮ Comparisons among some predictors of exponential distributions using Pitman closeness ⋮ A test for equality of variances with censored samples ⋮ Prediction intervals for future Weibull residual data ⋮ Pitman comparisons of predictors of censored observations from progressively censored samples for exponential distribution ⋮ Prediction of times to failure of censored units under generalized progressive hybrid censoring scheme ⋮ Comparative study for assessing the Pitman’s closeness of predictors based on exponential record data

Cites Work

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• Maximum likelihood prediction
• Tables of Expected Values of Order Statistics and Products of Order Statistics for Samples of Size Twenty and Less from the Normal Distribution
• Unbiased estimation of parameters by order statistics in the case of censored samples
• Best Linear Unbiased Prediction of Order Statistics in Location and Scale Families
• Linear predictors of future order statistics
• Best Linear Unbiased Prediction in the Generalized Linear Regression Model
• ESTIMATION OF THE MEAN AND STANDARD DEVIATION OF A NORMAL POPULATION FROM A CENSORED SAMPLE

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